The field of data mining has become accustomed to specifying constraints on patterns of
interest. A large number of systems and techniques has been developed for solving such …

Constrained Clustering allows to make the clustering task more accurate by integrating user
constraints, which can be instance-level or cluster-level constraints. Few works consider the …

One of the biggest challenges in the design of real-world decision support systems is
coming up with a good combinatorial optimization model. Often enough, accurate predictive …

Modern forms of computing systems are becoming progressively more complex, with an
increasing number of heterogeneous hardware and software components. As a result, it is …

Clustering is an unsupervised process which aims to discover regularities and underlying
structures in data. Constrained clustering extends clustering in such a way that expert …

Discovering the set of closed frequent patterns is one of the fundamental problems in Data
Mining. Recent Constraint Programming (CP) approaches for declarative itemset mining …

Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration,
the selection of patterns constitutes a major challenge. The Minimum Description Length …

We introduce MiningZinc, a declarative framework for constraint-based data mining.
MiningZinc consists of two key components: a language component and an execution …

In recent years, clustering has been extended to constrained clustering, so as to integrate
knowledge on objects or on clusters, but adding such constraints generally requires to …

Discriminative pattern mining is one of the most important techniques in data mining. This
challenging task is concerned with finding a set of patterns that occur with disproportionate …